Abstract-Pac-Man is a well-known, real-time computer game that provides an interesting platform for research. This paper describes an initial approach to developing an artificial agent that replaces the human to play a simplified version of Pac-Man. The agent is specified as a simple finite state machine and ruleset, with parameters that control the probability of movement by the agent given the constraints of the maze at some instant of time. In contrast to previous approaches, the agent represents a dynamic strategy for playing Pac-Man, rather than a pre-programmed maze-solving method. The agent adaptively "learns" through the application of population-based incremental learning (PBIL) to adjust the agents' parameters. Experimental results are presented that give insight into some of the complexities of the game, as well as highlighting the limitations and difficulties of the representation of the agent.

This paper outlines and evaluates the creation of a hybrid postmodernist method, using a rhizomic research structure to combine various research strategies within an interactive space of creative writing praxis. The model presented is suited to a practice-led researcher working within the traditional boundaries of academia.